Overview

Dataset statistics

Number of variables9
Number of observations850
Missing cells150
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.9 KiB
Average record size in memory72.2 B

Variable types

Numeric7
Categorical2

Alerts

age is highly overall correlated with employ and 2 other fieldsHigh correlation
employ is highly overall correlated with age and 1 other fieldsHigh correlation
address is highly overall correlated with ageHigh correlation
income is highly overall correlated with age and 3 other fieldsHigh correlation
debtinc is highly overall correlated with creddebt and 1 other fieldsHigh correlation
creddebt is highly overall correlated with income and 2 other fieldsHigh correlation
othdebt is highly overall correlated with income and 2 other fieldsHigh correlation
default has 150 (17.6%) missing valuesMissing
employ has 72 (8.5%) zerosZeros
address has 60 (7.1%) zerosZeros

Reproduction

Analysis started2024-01-11 16:34:55.917979
Analysis finished2024-01-11 16:35:18.689064
Duration22.77 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct37
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.029412
Minimum20
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-01-11T16:35:19.171120image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile23
Q129
median34
Q341
95-th percentile49
Maximum56
Range36
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.0414316
Coefficient of variation (CV)0.22956228
Kurtosis-0.65755007
Mean35.029412
Median Absolute Deviation (MAD)6
Skewness0.3350366
Sum29775
Variance64.664623
MonotonicityNot monotonic
2024-01-11T16:35:19.456599image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
29 51
 
6.0%
31 42
 
4.9%
39 41
 
4.8%
35 40
 
4.7%
34 38
 
4.5%
28 38
 
4.5%
41 36
 
4.2%
36 33
 
3.9%
27 33
 
3.9%
40 32
 
3.8%
Other values (27) 466
54.8%
ValueCountFrequency (%)
20 2
 
0.2%
21 12
 
1.4%
22 14
 
1.6%
23 21
2.5%
24 30
3.5%
25 25
2.9%
26 30
3.5%
27 33
3.9%
28 38
4.5%
29 51
6.0%
ValueCountFrequency (%)
56 2
 
0.2%
55 2
 
0.2%
54 4
 
0.5%
53 7
 
0.8%
52 9
1.1%
51 7
 
0.8%
50 11
1.3%
49 5
 
0.6%
48 19
2.2%
47 22
2.6%

ed
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
1
460 
2
235 
3
101 
4
49 
5
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters850
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 460
54.1%
2 235
27.6%
3 101
 
11.9%
4 49
 
5.8%
5 5
 
0.6%

Length

2024-01-11T16:35:19.726480image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-11T16:35:19.976350image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1 460
54.1%
2 235
27.6%
3 101
 
11.9%
4 49
 
5.8%
5 5
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 460
54.1%
2 235
27.6%
3 101
 
11.9%
4 49
 
5.8%
5 5
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 850
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 460
54.1%
2 235
27.6%
3 101
 
11.9%
4 49
 
5.8%
5 5
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 460
54.1%
2 235
27.6%
3 101
 
11.9%
4 49
 
5.8%
5 5
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 460
54.1%
2 235
27.6%
3 101
 
11.9%
4 49
 
5.8%
5 5
 
0.6%

employ
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5658824
Minimum0
Maximum33
Zeros72
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-01-11T16:35:20.235530image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q313
95-th percentile21.55
Maximum33
Range33
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.7778836
Coefficient of variation (CV)0.79126508
Kurtosis0.37920084
Mean8.5658824
Median Absolute Deviation (MAD)5
Skewness0.86266291
Sum7281
Variance45.939706
MonotonicityNot monotonic
2024-01-11T16:35:20.517558image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 72
 
8.5%
1 59
 
6.9%
4 57
 
6.7%
6 53
 
6.2%
9 52
 
6.1%
2 50
 
5.9%
3 50
 
5.9%
5 49
 
5.8%
7 45
 
5.3%
10 38
 
4.5%
Other values (23) 325
38.2%
ValueCountFrequency (%)
0 72
8.5%
1 59
6.9%
2 50
5.9%
3 50
5.9%
4 57
6.7%
5 49
5.8%
6 53
6.2%
7 45
5.3%
8 38
4.5%
9 52
6.1%
ValueCountFrequency (%)
33 2
 
0.2%
31 3
0.4%
30 3
0.4%
29 2
 
0.2%
28 1
 
0.1%
27 3
0.4%
26 1
 
0.1%
25 4
0.5%
24 5
0.6%
23 6
0.7%

address
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3717647
Minimum0
Maximum34
Zeros60
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-01-11T16:35:20.786320image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q312
95-th percentile22.55
Maximum34
Range34
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8950164
Coefficient of variation (CV)0.8236037
Kurtosis0.25679934
Mean8.3717647
Median Absolute Deviation (MAD)5
Skewness0.92379061
Sum7116
Variance47.541251
MonotonicityNot monotonic
2024-01-11T16:35:21.061752image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 71
 
8.4%
2 71
 
8.4%
0 60
 
7.1%
4 58
 
6.8%
3 55
 
6.5%
6 50
 
5.9%
8 49
 
5.8%
9 45
 
5.3%
5 43
 
5.1%
7 41
 
4.8%
Other values (22) 307
36.1%
ValueCountFrequency (%)
0 60
7.1%
1 71
8.4%
2 71
8.4%
3 55
6.5%
4 58
6.8%
5 43
5.1%
6 50
5.9%
7 41
4.8%
8 49
5.8%
9 45
5.3%
ValueCountFrequency (%)
34 1
 
0.1%
31 2
 
0.2%
30 1
 
0.1%
29 1
 
0.1%
27 4
 
0.5%
26 10
1.2%
25 9
1.1%
24 4
 
0.5%
23 11
1.3%
22 9
1.1%

income
Real number (ℝ)

Distinct129
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.675294
Minimum13
Maximum446
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-01-11T16:35:21.352969image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile17
Q124
median35
Q355.75
95-th percentile115.55
Maximum446
Range433
Interquartile range (IQR)31.75

Descriptive statistics

Standard deviation38.543054
Coefficient of variation (CV)0.82576992
Kurtosis22.485763
Mean46.675294
Median Absolute Deviation (MAD)13
Skewness3.7007613
Sum39674
Variance1485.567
MonotonicityNot monotonic
2024-01-11T16:35:21.621469image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 28
 
3.3%
25 27
 
3.2%
26 27
 
3.2%
22 26
 
3.1%
27 25
 
2.9%
20 25
 
2.9%
24 24
 
2.8%
28 24
 
2.8%
18 23
 
2.7%
23 23
 
2.7%
Other values (119) 598
70.4%
ValueCountFrequency (%)
13 1
 
0.1%
14 8
 
0.9%
15 10
 
1.2%
16 20
2.4%
17 16
1.9%
18 23
2.7%
19 15
1.8%
20 25
2.9%
21 28
3.3%
22 26
3.1%
ValueCountFrequency (%)
446 1
0.1%
324 1
0.1%
266 1
0.1%
254 1
0.1%
253 1
0.1%
249 1
0.1%
242 1
0.1%
234 1
0.1%
221 1
0.1%
220 1
0.1%

debtinc
Real number (ℝ)

Distinct245
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.171647
Minimum0.1
Maximum41.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-01-11T16:35:21.897284image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.9
Q15.1
median8.7
Q313.8
95-th percentile23.51
Maximum41.3
Range41.2
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation6.7194413
Coefficient of variation (CV)0.66060504
Kurtosis1.3876939
Mean10.171647
Median Absolute Deviation (MAD)4.2
Skewness1.1249989
Sum8645.9
Variance45.150891
MonotonicityNot monotonic
2024-01-11T16:35:22.178788image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.4 12
 
1.4%
4.4 10
 
1.2%
5 10
 
1.2%
6.7 10
 
1.2%
4.5 10
 
1.2%
10.5 9
 
1.1%
6.4 9
 
1.1%
3.7 9
 
1.1%
7.2 9
 
1.1%
4.1 8
 
0.9%
Other values (235) 754
88.7%
ValueCountFrequency (%)
0.1 1
 
0.1%
0.4 1
 
0.1%
0.6 2
 
0.2%
0.7 1
 
0.1%
0.8 1
 
0.1%
0.9 4
0.5%
1 2
 
0.2%
1.1 4
0.5%
1.2 6
0.7%
1.3 3
0.4%
ValueCountFrequency (%)
41.3 1
0.1%
36.6 1
0.1%
35.3 1
0.1%
33.4 1
0.1%
33.3 1
0.1%
32.5 2
0.2%
32.4 1
0.1%
32.3 1
0.1%
30.8 1
0.1%
30.7 1
0.1%

creddebt
Real number (ℝ)

Distinct842
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5768047
Minimum0.011696
Maximum20.56131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-01-11T16:35:22.471048image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.011696
5-th percentile0.108927
Q10.382176
median0.8850915
Q31.8984405
95-th percentile5.3486741
Maximum20.56131
Range20.549614
Interquartile range (IQR)1.5162645

Descriptive statistics

Standard deviation2.12584
Coefficient of variation (CV)1.3481948
Kurtosis19.500145
Mean1.5768047
Median Absolute Deviation (MAD)0.6067105
Skewness3.7018998
Sum1340.284
Variance4.5191958
MonotonicityNot monotonic
2024-01-11T16:35:22.761469image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.105 2
 
0.2%
0.402192 2
 
0.2%
0.44982 2
 
0.2%
0.2254 2
 
0.2%
0.130416 2
 
0.2%
0.62208 2
 
0.2%
0.085785 2
 
0.2%
0.43344 2
 
0.2%
0.272636 1
 
0.1%
6.91152 1
 
0.1%
Other values (832) 832
97.9%
ValueCountFrequency (%)
0.011696 1
0.1%
0.014835 1
0.1%
0.02205 1
0.1%
0.024528 1
0.1%
0.024576 1
0.1%
0.025074 1
0.1%
0.029412 1
0.1%
0.029898 1
0.1%
0.030212 1
0.1%
0.030492 1
0.1%
ValueCountFrequency (%)
20.56131 1
0.1%
16.03147 1
0.1%
15.791776 1
0.1%
15.01668 1
0.1%
14.5962 1
0.1%
14.231448 1
0.1%
13.5525 1
0.1%
11.359392 1
0.1%
10.67934 1
0.1%
9.8766 1
0.1%

othdebt
Real number (ℝ)

Distinct848
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0787894
Minimum0.045584
Maximum35.1975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2024-01-11T16:35:23.038307image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.045584
5-th percentile0.35989955
Q11.045942
median2.003243
Q33.9030008
95-th percentile9.4811046
Maximum35.1975
Range35.151916
Interquartile range (IQR)2.8570588

Descriptive statistics

Standard deviation3.3988033
Coefficient of variation (CV)1.1039415
Kurtosis16.635286
Mean3.0787894
Median Absolute Deviation (MAD)1.132505
Skewness3.2060225
Sum2616.971
Variance11.551864
MonotonicityNot monotonic
2024-01-11T16:35:23.376904image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.8234 2
 
0.2%
3.166086 2
 
0.2%
5.008608 1
 
0.1%
1.755775 1
 
0.1%
0.775372 1
 
0.1%
0.682176 1
 
0.1%
3.9468 1
 
0.1%
0.364364 1
 
0.1%
4.51248 1
 
0.1%
4.799355 1
 
0.1%
Other values (838) 838
98.6%
ValueCountFrequency (%)
0.045584 1
0.1%
0.05295 1
0.1%
0.089488 1
0.1%
0.100926 1
0.1%
0.10752 1
0.1%
0.129582 1
0.1%
0.15012 1
0.1%
0.1563 1
0.1%
0.160983 1
0.1%
0.163863 1
0.1%
ValueCountFrequency (%)
35.1975 1
0.1%
27.0336 1
0.1%
23.104224 1
0.1%
20.615868 1
0.1%
18.26913 1
0.1%
18.257382 1
0.1%
17.79899 1
0.1%
17.2038 1
0.1%
17.184552 1
0.1%
16.668126 1
0.1%

default
Categorical

Distinct2
Distinct (%)0.3%
Missing150
Missing (%)17.6%
Memory size6.8 KiB
0.0
517 
1.0
183 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 517
60.8%
1.0 183
 
21.5%
(Missing) 150
 
17.6%

Length

2024-01-11T16:35:23.690727image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-11T16:35:23.928728image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 517
73.9%
1.0 183
 
26.1%

Most occurring characters

ValueCountFrequency (%)
0 1217
58.0%
. 700
33.3%
1 183
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1217
86.9%
1 183
 
13.1%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1217
58.0%
. 700
33.3%
1 183
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1217
58.0%
. 700
33.3%
1 183
 
8.7%

Interactions

2024-01-11T16:35:15.915862image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:03.969066image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:05.967125image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:07.965015image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:10.187068image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:12.099179image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:13.978312image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:16.219726image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:04.270199image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:06.281423image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:08.243774image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:10.477824image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:12.379418image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:14.229072image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:16.500533image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:04.483112image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:06.576806image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:08.754460image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:10.747837image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:12.648581image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:14.507217image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:16.798990image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:04.793972image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:06.884021image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:09.055126image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:11.032019image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:12.926809image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:14.781146image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:17.066743image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:05.075986image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:07.124465image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:09.334704image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:11.293556image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:13.187383image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:15.054337image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:17.348191image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:05.365129image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:07.401424image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:09.620800image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:11.552464image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:13.448291image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:15.338749image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:17.628846image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:05.654441image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:07.677083image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:09.895579image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:11.815933image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:13.699859image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-11T16:35:15.616697image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2024-01-11T16:35:24.136769image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ageemployaddressincomedebtinccreddebtothdebteddefault
age1.0000.5470.5610.574-0.0010.2950.3340.0290.179
employ0.5471.0000.3250.707-0.0700.3180.3540.0540.310
address0.5610.3251.0000.345-0.0140.2060.1910.0690.200
income0.5740.7070.3451.000-0.0210.5030.5570.1760.111
debtinc-0.001-0.070-0.014-0.0211.0000.6150.7130.0000.404
creddebt0.2950.3180.2060.5030.6151.0000.6130.0610.213
othdebt0.3340.3540.1910.5570.7130.6131.0000.0860.114
ed0.0290.0540.0690.1760.0000.0610.0861.0000.103
default0.1790.3100.2000.1110.4040.2130.1140.1031.000

Missing values

2024-01-11T16:35:18.032662image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-11T16:35:18.482852image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ageedemployaddressincomedebtinccreddebtothdebtdefault
041317121769.311.3593925.0086081.0
12711063117.31.3622024.0007980.0
24011514555.50.8560752.1689250.0
341115141202.92.6587200.8212800.0
4242202817.31.7874363.0565641.0
5412552510.20.3927002.1573000.0
63912096730.63.83387416.6681260.0
74311211383.60.1285921.2394080.0
8241341924.41.3583483.2776521.0
93610132519.72.7777002.1473000.0
ageedemployaddressincomedebtinccreddebtothdebtdefault
8403521116232.49.70250410.385496NaN
8413541012458.51.0404002.784600NaN
84251415302613.62.0119841.524016NaN
84336152277.00.7238701.166130NaN
84423134133.10.0455390.357461NaN
8453411215322.70.2393280.624672NaN
84632212111165.74.0267082.585292NaN
84748113113810.80.7223043.381696NaN
848352111247.80.4174561.454544NaN
84937120134112.90.8991304.389870NaN